Open Access

ARTICLE

A Big Data Approach to Black Friday Sales

Mazhar Javed Awan^1,2,*, Mohd Shafry Mohd Rahim², Haitham Nobanee^3,4,5, Awais Yasin⁶, Osamah Ibrahim Khalaf⁷, Umer Ishfaq²

1 Department of Software Engineering, University of Management and Technology, Lahore, Pakistan
2 School of Computing, Faculty of Engineering, University Teknologi Malaysia, Johor, Malaysia
3Collage of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates
4 Oxford Center for Islamic Studies, the University of Oxford, Marston Road, Oxford, UK
5 The University of Liverpool Management School, the University of Liverpool, Liverpool, UK
6 Department of Computer Engineering, National University of Technology, Islamabad, Pakistan
7 AlNahrain Nanorenewable Energy Research Centre, Al-Nahrain University, Baghdad, Iraq

* Corresponding Author: Mazhar Javed Awan. Email:

Intelligent Automation & Soft Computing 2021, 27(3), 785-797. https://doi.org/10.32604/iasc.2021.014216

Received 07 September 2020; Accepted 22 December 2020; Issue published 01 March 2021

Abstract

Retail companies recognize the need to analyze and predict their sales and customer behavior against their products and product categories. Our study aims to help retail companies create personalized deals and promotions for their customers, even during the COVID-19 pandemic, through a big data framework that allows them to handle massive sales volumes with more efficient models. In this paper, we used Black Friday sales data taken from a dataset on the Kaggle website, which contains nearly 550,000 observations analyzed with 10 features: qualitative and quantitative. The class label is purchases and sales (in U.S. dollars). Because the predictor label is continuous, regression models are suited in this case. Using the Apache Spark big data framework, which uses the MLlib machine learning library, we trained two machine learning models: linear regression and random forest. These machine learning algorithms were used to predict future pricing and sales. We first implemented a linear regression model and a random forest model without using the Spark framework and achieved accuracies of 68% and 74%, respectively. Then, we trained these models on the Spark machine learning big data framework where we achieved an accuracy of 72% for the linear regression model and 81% for the random forest model.

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Citations